The Countryside Survey (CS) of Great Britain provides a globally unique series of datasets, consisting of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to monitor ecological and land use change in Britain using standardised procedures for recording ecological data from representative 1 km squares throughout the country. The same sites, with some additional squares, were used for subsequent surveys of vegetation undertaken in 1990, 1998 and 2007, with the intention of future surveys. Other data records include soils, freshwater habitats and invertebrates, and land cover and landscape feature diversity and extents. These data have been recorded in the same locations on analogous dates. However, the present paper describes only the details of the vegetation surveys. The survey design is a series of gridded, stratified, randomly selected 1 km squares taken as representative of classes derived from a statistical environmental classification of Britain. In the 1978 survey, 256 one-kilometre sample squares were recorded, increasing to 506 in 1990, 569 in 1998 and 591 in 2007. Initially each square contained up to 11 dispersed vegetation plots but additional plots were later placed in different features so that eventually up to 36 additional sampling plots were recorded, all of which can be relocated where possible (unless the plot has been lost, for example as a consequence of building work), providing a total of 16 992 plots by 2007. Plots are estimated to have a precise relocation accuracy of 85 %. A range of plots located in different land cover types and landscape features (for example, field boundaries) are included. Although a range of analyses have already been carried out, with changes in the vegetation being related to a range of drivers at local and national scales, there is major potential for further analyses, for example in relation to climate change. Although the precise locations of the plots are restricted, largely for reasons of landowner confidentiality, sample sites are intended to be representative of larger areas, and many potential opportunities for further analyses remain. Data from each of the survey years (1978, 1990, 1998, 2007) are available via the following DOIs: Countryside Survey 1978 vegetation plot data (https://doi.org/10.5285/67bbfabb-d981-4ced-b7e7-225205de9c96), Countryside Survey 1990 vegetation plot data (https://doi.org/10.5285/26e79792-5ffc-4116-9ac7-72193dd7f191), Countryside Survey 1998 vegetation plot data (https://doi.org/10.5285/07896bb2-7078-468c-b56d-fb8b41d47065), Countryside Survey 2007 vegetation plot data (https://doi.org/10.5285/57f97915-8ff1-473b-8c77-2564cbd747bc).

Pollinating insects provide economic value by improving crop yield. They are also functionally and culturally important across ecosystems outside of cropland. To understand landscape‐level drivers of pollinator declines, and guide policy and intervention to reverse declines, studies must cover (a) multiple insect and plant taxa and (b) a range of agricultural and semi‐natural land uses. Furthermore, in an era of woodland restoration initiatives and rewilding ideologies, the contribution of woodland and woody linear features (WLFs; e.g. hedgerows) to pollinator abundance demands further investigation. We demonstrate fine‐scale analysis of high‐quality, co‐located measurements from a national environmental survey. We relate pollinator transect counts to ground‐truth habitat and WLF maps across 300 1 km squares in Wales, UK. We look at effects of habitat type, flower cover, WLF density and habitat diversity on summer abundance (July and August) of eight insect groups, representing three insect orders (Lepidoptera, Hymenoptera and Diptera). Compared with improved grassland (the dominant habitat in Wales), pollinator abundance is consistently higher in cropland and woodland—especially broadleaved woodland. For mining bees and two hoverfly groups, abundance is predicted to be at least 1.5× higher in woodland ecosystems than elsewhere. Furthermore, we estimate contributions of WLFs to abundance in agriculturally improved habitats to be up to 14% for honeybees and up to 21% for hoverflies. The abundance of all insect groups increases with flower cover, which is a key mechanism through which woodland, cropland and grassland support pollinators. Importantly, we observe diminishing returns of increasing flower cover for abundance of non‐Apis pollinator groups, expecting roughly twice the increase in abundance per % flower cover from 0% to 5%, as compared with 10% to 15%. However, the shape of the relationship was inverted for honeybees, which showed steeper increases in abundance at higher flower cover. Synthesis and applications. We provide a holistic view of the drivers of pollinator abundance in Wales, in which flower cover, woodland, hedgerows and cropland are critical. We propose a key role for woodland creation, hedge‐laying and farmland heterogeneity within future land management incentive schemes. Finally, we suggest targeting of interventions to maximise benefits for non‐Apis pollinators. Specifically, increasing floral provision in areas where existing flower cover is low—for example, in flower‐poor improved grasslands—could effectively increase pollinator abundance and diversity while prioritising wild over managed species.

Land cover is a key environmental variable, underpinning widespread environmental research and decision making. The UK Centre for Ecology and Hydrology (UKCEH) has provided reliable land cover information since the early 1990s; this supports multiple scientific, government and commercial objectives. Recent advances in computation and satellite data availability have enabled annual UKCEH land cover maps since 2017. Here, we introduce the latest, annual UK Land Cover Map representing 2021 (LCM2021), and we describe its production and validation. LCM2021 methods replicate those of LCM2017 to LCM2020 with minor deviations in cloud-masking processes and training data sourcing to enhance accuracy. LCM2021 is based on the classification of satellite and spatial context data into 21 land cover or habitat classes, from which a product suite is derived. The production of LCM2021 involved three highly automated key stages: pre-processing of input data, image classification and production of the final data products. Google Earth Engine scripts were used to create an input data stack of satellite and context data. A set of training areas was created based on data harvested from historic UKCEH land cover maps. The training data were used to construct a random forest classifier, which yielded classified images. Compiled results were validated against 35 182 reference samples, with correspondence tables indicating variable class accuracy and an overall accuracy of 82.6 % for the 21-class data and 86.5 % at a 10-aggregated-classes level. The UK Land Cover Map product suite includes a set of raster products in various projections, thematic and spatial resolutions (10 m, 25 m and 1 km), and land–parcel or vector products. The data are provided in 21-class (all configurations) and aggregated 10-class (1 km raster products only) versions. All raster products are freely available for academic and non-commercial research. The data for Great Britain (GB) are provided in the British National Grid projection (EPSG: 27700) and the Northern Ireland (NI) data are in the TM75 Irish Grid (EPSG: 29903). Information on how to access the data is given in the “Data availability” section of the paper.

The Woodland Survey of Great Britain is a unique data set, consisting of a detailed range of ecological measurements at a national scale, covering a time span of 30 years. A set of 103 woods spread across Britain were first surveyed in 1971, which were again surveyed in 2000–2003 (for convenience referred to subsequently as the "2001 survey"). Standardised methods of describing the trees, shrubs, ground flora, soils and general habitats present were used for both sets of surveys. The sample of 1648 plots spread through 103 woodland sites located across Britain makes it probably the most extensive quantitative ecological woodland survey undertaken in Britain; it is also notable for the range of sites that have been revisited after such a long interval. The data set provides a unique opportunity to explore the effects of a range of potential drivers of woodland change that operated between 1971 and 2001. The data set is available in four discrete parts, which have been assigned the following DOIs: doi:10.5285/4d93f9ac-68e3-49cf-8a41-4d02a7ead81a (Kirby et al., 2013b), doi:10.5285/d6409d40-58fe-4fa7-b7c8-71a105b965b4 (Kirby et al., 2013d), doi:10.5285/fb1e474d-456b-42a9-9a10-a02c35af10d2 (Kirby et al., 2013c), doi:10.5285/2d023ce9-6dbe-4b4f-a0cd-34768e1455ae (Kirby et al., 2013a).

The Countryside Survey (CS) of Great Britain (GB) provides a unique and statistically robust series of datasets, consisting of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to provide a baseline for ecological and land use change monitoring in the rural environment of GB, following a stratified random design, based on 1 km squares. Originally, eight random 1 km squares were drawn from each of 32 environmental classes, thus comprising 256 sample squares in the 1978 survey. The number of these sites increased to 382 in 1984, 506 in 1990, 569 in 1998 and 591 in 2007. Detailed information regarding vegetation types and land use was mapped in all five surveys, allowing reporting by defined standard habitat classifications. Additionally, point and linear landscape features (such as trees and hedgerows) are available from all surveys after 1978. From these stratified, randomly located sample squares, information can be converted into national estimates, with associated error terms. Other data, relating to soils, freshwater and vegetation, were also sampled on analogous dates. However, the present paper describes only the surveys of landscape features and habitats. The resulting datasets provide a unique, comprehensive, quantitative ecological coverage of extent and change in these features in GB. Basic results are presented and their implications discussed. However, much opportunity for further analyses remains. Data from each of the survey years are available via the following DOIs: Landscape area data 1978: https://doi.org/10.5285/86c017ba-dc62-46f0-ad13-c862bf31740e, 1984: https://doi.org/10.5285/b656bb43-448d-4b2c-aade-7993aa243ea3, 1990: https://doi.org/10.5285/94f664e5-10f2-4655-bfe6-44d745f5dca7, 1998: https://doi.org/10.5285/1e050028-5c55-42f4-a0ea-c895d827b824, and 2007: https://doi.org/10.5285/bf189c57-61eb-4339-a7b3-d2e81fdde28d; Landscape linear feature data 1984: https://doi.org/10.5285/a3f5665c-94b2-4c46-909e-a98be97857e5, 1990: https://doi.org/10.5285/311daad4-bc8c-485a-bc8a-e0d054889219, 1998: https://doi.org/10.5285/8aaf6f8c-c245-46bb-8a2a-f0db012b2643 and 2007: https://doi.org/10.5285/e1d31245-4c0a-4dee-b36c-b23f1a697f88, Landscape point feature data 1984: https://doi.org/10.5285/124b872e-036e-4dd3-8316-476b5f42c16e, 1990: https://doi.org/10.5285/1481bc63-80d7-4d18-bcba-8804aa0a9e1b, 1998: https://doi.org/10.5285/ed10944f-40c8-4913-b3f5-13c8e844e153 and 2007: https://doi.org/10.5285/55dc5fd7-d3f7-4440-b8a7-7187f8b0550b.

Peatlands are important reserves of terrestrial carbon and biodiversity, and given that many peatlands across the UK and Europe exist in a degraded state, their conservation is a major area of concern and a focus of considerable research. Aerial surveys are valuable tools for habitat mapping and conservation and provide useful insights into their condition. We investigate how SfM photogrammetry-derived topography and habitat classes may be used to construct an estimate of carbon loss from erosion features in a remote blanket bog habitat. An autonomous, unmanned, aerial, fixed-wing remote sensing platform (Quest UAV 300™) collected imagery over Moor House, in the Upper Teesdale National Nature Reserve, a site with a high degree of peatland erosion. The images were used to generate point clouds into orthomosaics and digital surface models using SfM photogrammetry techniques, georeferenced and subsequently used to classify vegetation and peatland features. A classification of peatbog feature types was developed using a random forest classification model trained on field survey data and applied to UAV-captured products including the orthomosaic, digital surface model and derived surfaces such as topographic index, slope and aspect maps. Using the area classified as eroded peat and the derived digital surface model, we estimated a loss of 438 tonnes of carbon from a single gully. The UAV system was relatively straightforward to deploy in such a remote and unimproved area. SfM photogrammetry, imagery and random forest modelling obtained classification accuracies of between 42% and 100%, and was able to discern between bare peat, saturated bog and sphagnum habitats. This paper shows what can be achieved with low-cost UAVs equipped with consumer grade camera equipment and relatively straightforward ground control, and demonstrates their potential for the carbon and peatland conservation research community.